User:Dnlbreen/Maximally Informative Dimensions (MID)

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Maximally Informative Dimensions, or MID, refers to a computational method based on information theoretic principles used to calculate a neuron's receptive field. Maximizing information refers to maximizing the Kullbeck-Leibler divergence between two probability distributions - the prior distribution of reduced stimuli and the distribution of reduced stimuli conditional on the neuronal response - by means of appropriately selecting a neuron's receptive field^[1]. When analyzing neural responses, Shannon's mutual information becomes useful because it provides a rigorous way of comparing the two probability distributions^[1]. As an optimization scheme, MID has the advantage that it does not depend on any specific statistical properties of the stimulus ensemble.